Conventional industrial computing environments are organized in a multi-layer architecture wherein data is collected by devices such as programmable logic controllers at a control layer. This data is then passed up through one or more higher layers for analysis and storage at a management layer. The data transfer required by this architecture is significant because data corresponding to potentially millions of sample points must be transferred between the layers. Thus, the bandwidth of the network connecting the layers provides bottleneck for how much data can between analyzed and stored. Additionally, the transfer of data between the layers reduces the visibility and readiness of data which, in turn, limits the effectiveness of extracting insights about the embedded controller's behavior. Moreover, conventional systems do not make use of controller context to obtain deeper analytic insights regarding activity performed by the embedded device, as well as the operational environment. Without such insights, the decision making for the system is inefficient.
Recent advances in control layer devices have addressed some of the inefficiencies of the system by providing enhanced storage and processing capabilities within the device. However, the capabilities are generally underutilized in conventional systems which force the control layer device to fit within the paradigm of the multi-layer architecture discussed above. For example, each control layer device has privileged access to process data (e.g., behaviors) and controller logic. However, only a limited amount of this information can be passed to higher layers due to the bandwidth limitations of the underlying network. Moreover, conventional architectures force all decision making of control layer devices to be centralized at higher layers because each control layer device has no knowledge of the processes or data being generated by its peers.
At a larger scale, today's systems can produce massive amounts of data which can only be handled by parallel computing strategies of which distributed analytics is a key component. In today's systems, distributed analytics at the lower level is not possible since there is no distributed data management as part of industrial automation setup.